Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations3110
Missing cells0
Missing cells (%)0.0%
Duplicate rows464
Duplicate rows (%)14.9%
Total size in memory340.2 KiB
Average record size in memory112.0 B

Variable types

Categorical1
Numeric12

Alerts

Dataset has 464 (14.9%) duplicate rowsDuplicates
alcohol is highly overall correlated with densityHigh correlation
chlorides is highly overall correlated with density and 4 other fieldsHigh correlation
density is highly overall correlated with alcohol and 3 other fieldsHigh correlation
fixed acidity is highly overall correlated with chlorides and 2 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
residual sugar is highly overall correlated with typeHigh correlation
sulphates is highly overall correlated with typeHigh correlation
total sulfur dioxide is highly overall correlated with chlorides and 2 other fieldsHigh correlation
type is highly overall correlated with chlorides and 6 other fieldsHigh correlation
volatile acidity is highly overall correlated with chlorides and 1 other fieldsHigh correlation
citric acid has 138 (4.4%) zeros Zeros

Reproduction

Analysis started2024-10-31 21:23:11.384689
Analysis finished2024-10-31 21:23:24.540744
Duration13.16 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.6 KiB
Moscatel
1588 
Syrah
1522 

Length

Max length8
Median length8
Mean length6.5318328
Min length5

Characters and Unicode

Total characters20314
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMoscatel
2nd rowMoscatel
3rd rowMoscatel
4th rowMoscatel
5th rowMoscatel

Common Values

ValueCountFrequency (%)
Moscatel 1588
51.1%
Syrah 1522
48.9%

Length

2024-10-31T18:23:24.653218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-31T18:23:24.741987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
moscatel 1588
51.1%
syrah 1522
48.9%

Most occurring characters

ValueCountFrequency (%)
a 3110
15.3%
M 1588
7.8%
s 1588
7.8%
o 1588
7.8%
c 1588
7.8%
t 1588
7.8%
e 1588
7.8%
l 1588
7.8%
S 1522
7.5%
y 1522
7.5%
Other values (2) 3044
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3110
15.3%
M 1588
7.8%
s 1588
7.8%
o 1588
7.8%
c 1588
7.8%
t 1588
7.8%
e 1588
7.8%
l 1588
7.8%
S 1522
7.5%
y 1522
7.5%
Other values (2) 3044
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3110
15.3%
M 1588
7.8%
s 1588
7.8%
o 1588
7.8%
c 1588
7.8%
t 1588
7.8%
e 1588
7.8%
l 1588
7.8%
S 1522
7.5%
y 1522
7.5%
Other values (2) 3044
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3110
15.3%
M 1588
7.8%
s 1588
7.8%
o 1588
7.8%
c 1588
7.8%
t 1588
7.8%
e 1588
7.8%
l 1588
7.8%
S 1522
7.5%
y 1522
7.5%
Other values (2) 3044
15.0%

fixed acidity
Real number (ℝ)

High correlation 

Distinct90
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3284566
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.6 KiB
2024-10-31T18:23:24.836683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.6
Q16.4
median7
Q37.9
95-th percentile10.4
Maximum15.9
Range12.1
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.4523095
Coefficient of variation (CV)0.19817399
Kurtosis1.9929427
Mean7.3284566
Median Absolute Deviation (MAD)0.7
Skewness1.2753696
Sum22791.5
Variance2.1092028
MonotonicityNot monotonic
2024-10-31T18:23:24.947844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 155
 
5.0%
6.8 149
 
4.8%
6.4 143
 
4.6%
6.7 119
 
3.8%
6 114
 
3.7%
7.2 114
 
3.7%
7 114
 
3.7%
7.1 111
 
3.6%
6.9 103
 
3.3%
6.5 100
 
3.2%
Other values (80) 1888
60.7%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4.4 3
 
0.1%
4.6 1
 
< 0.1%
4.7 6
 
0.2%
4.8 7
 
0.2%
4.9 5
 
0.2%
5 19
0.6%
5.1 14
0.5%
5.2 16
0.5%
ValueCountFrequency (%)
15.9 1
 
< 0.1%
13.8 1
 
< 0.1%
13.3 1
 
< 0.1%
13 1
 
< 0.1%
12.8 3
0.1%
12.7 4
0.1%
12.6 3
0.1%
12.5 6
0.2%
12.4 3
0.1%
12.3 1
 
< 0.1%

volatile acidity
Real number (ℝ)

High correlation 

Distinct171
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40281833
Minimum0.085
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.6 KiB
2024-10-31T18:23:25.051966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.085
5-th percentile0.17
Q10.26
median0.35
Q30.53
95-th percentile0.74775
Maximum1.58
Range1.495
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.18984357
Coefficient of variation (CV)0.47128832
Kurtosis1.1248979
Mean0.40281833
Median Absolute Deviation (MAD)0.12
Skewness1.0170516
Sum1252.765
Variance0.036040583
MonotonicityNot monotonic
2024-10-31T18:23:25.164217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 118
 
3.8%
0.24 102
 
3.3%
0.22 97
 
3.1%
0.27 88
 
2.8%
0.26 87
 
2.8%
0.32 86
 
2.8%
0.3 85
 
2.7%
0.31 84
 
2.7%
0.36 79
 
2.5%
0.29 70
 
2.3%
Other values (161) 2214
71.2%
ValueCountFrequency (%)
0.085 1
 
< 0.1%
0.09 1
 
< 0.1%
0.105 4
 
0.1%
0.11 5
 
0.2%
0.12 10
 
0.3%
0.13 8
 
0.3%
0.14 15
 
0.5%
0.145 2
 
0.1%
0.15 31
1.0%
0.16 48
1.5%
ValueCountFrequency (%)
1.58 1
< 0.1%
1.33 2
0.1%
1.24 1
< 0.1%
1.185 1
< 0.1%
1.18 1
< 0.1%
1.13 1
< 0.1%
1.115 1
< 0.1%
1.1 1
< 0.1%
1.09 1
< 0.1%
1.07 1
< 0.1%

citric acid
Real number (ℝ)

Zeros 

Distinct83
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28375884
Minimum0
Maximum1
Zeros138
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size48.6 KiB
2024-10-31T18:23:25.277025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.2
median0.28
Q30.36
95-th percentile0.54
Maximum1
Range1
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.15469282
Coefficient of variation (CV)0.54515594
Kurtosis0.51139828
Mean0.28375884
Median Absolute Deviation (MAD)0.08
Skewness0.30860124
Sum882.49
Variance0.023929868
MonotonicityNot monotonic
2024-10-31T18:23:25.392174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 152
 
4.9%
0.3 143
 
4.6%
0 138
 
4.4%
0.26 130
 
4.2%
0.32 126
 
4.1%
0.27 123
 
4.0%
0.24 114
 
3.7%
0.29 109
 
3.5%
0.25 88
 
2.8%
0.33 87
 
2.8%
Other values (73) 1900
61.1%
ValueCountFrequency (%)
0 138
4.4%
0.01 37
 
1.2%
0.02 52
 
1.7%
0.03 30
 
1.0%
0.04 32
 
1.0%
0.05 21
 
0.7%
0.06 26
 
0.8%
0.07 20
 
0.6%
0.08 33
 
1.1%
0.09 37
 
1.2%
ValueCountFrequency (%)
1 2
0.1%
0.91 2
0.1%
0.86 1
 
< 0.1%
0.82 1
 
< 0.1%
0.79 1
 
< 0.1%
0.78 2
0.1%
0.76 1
 
< 0.1%
0.75 1
 
< 0.1%
0.74 4
0.1%
0.73 4
0.1%

residual sugar
Real number (ℝ)

High correlation 

Distinct227
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4713826
Minimum0.7
Maximum22.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.6 KiB
2024-10-31T18:23:25.496879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.2
Q11.9
median2.4
Q36.075
95-th percentile13.7
Maximum22.6
Range21.9
Interquartile range (IQR)4.175

Descriptive statistics

Standard deviation4.0701145
Coefficient of variation (CV)0.91025861
Kurtosis1.7679741
Mean4.4713826
Median Absolute Deviation (MAD)0.8
Skewness1.616036
Sum13906
Variance16.565832
MonotonicityNot monotonic
2024-10-31T18:23:25.606905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 187
 
6.0%
1.8 154
 
5.0%
2.1 140
 
4.5%
1.9 138
 
4.4%
2.2 137
 
4.4%
2.3 120
 
3.9%
2.4 104
 
3.3%
2.5 100
 
3.2%
1.6 96
 
3.1%
1.7 94
 
3.0%
Other values (217) 1840
59.2%
ValueCountFrequency (%)
0.7 2
 
0.1%
0.8 5
 
0.2%
0.9 16
 
0.5%
1 29
 
0.9%
1.1 52
1.7%
1.15 1
 
< 0.1%
1.2 74
2.4%
1.3 55
1.8%
1.4 84
2.7%
1.45 1
 
< 0.1%
ValueCountFrequency (%)
22.6 1
 
< 0.1%
20.3 1
 
< 0.1%
19.95 1
 
< 0.1%
19.4 1
 
< 0.1%
19.3 3
0.1%
19.25 2
0.1%
18.75 1
 
< 0.1%
18.5 1
 
< 0.1%
18.4 1
 
< 0.1%
18.35 1
 
< 0.1%

chlorides
Real number (ℝ)

High correlation 

Distinct191
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.065720579
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.6 KiB
2024-10-31T18:23:25.711936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.03
Q10.042
median0.057
Q30.08
95-th percentile0.11355
Maximum0.611
Range0.602
Interquartile range (IQR)0.038

Descriptive statistics

Standard deviation0.042082407
Coefficient of variation (CV)0.64032313
Kurtosis42.888796
Mean0.065720579
Median Absolute Deviation (MAD)0.019
Skewness5.130326
Sum204.391
Variance0.001770929
MonotonicityNot monotonic
2024-10-31T18:23:25.820612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.036 76
 
2.4%
0.048 75
 
2.4%
0.044 74
 
2.4%
0.05 69
 
2.2%
0.08 65
 
2.1%
0.047 63
 
2.0%
0.042 61
 
2.0%
0.041 56
 
1.8%
0.076 55
 
1.8%
0.04 54
 
1.7%
Other values (181) 2462
79.2%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.012 2
 
0.1%
0.013 1
 
< 0.1%
0.014 2
 
0.1%
0.015 4
0.1%
0.016 1
 
< 0.1%
0.017 3
0.1%
0.018 4
0.1%
0.019 1
 
< 0.1%
0.02 6
0.2%
ValueCountFrequency (%)
0.611 1
 
< 0.1%
0.61 1
 
< 0.1%
0.467 1
 
< 0.1%
0.464 1
 
< 0.1%
0.422 1
 
< 0.1%
0.415 3
0.1%
0.414 2
0.1%
0.413 1
 
< 0.1%
0.403 1
 
< 0.1%
0.401 1
 
< 0.1%

free sulfur dioxide
Real number (ℝ)

High correlation 

Distinct99
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.665916
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.6 KiB
2024-10-31T18:23:25.930049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q112
median23
Q335
95-th percentile57
Maximum289
Range288
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.412649
Coefficient of variation (CV)0.67843473
Kurtosis17.894867
Mean25.665916
Median Absolute Deviation (MAD)11
Skewness2.056841
Sum79821
Variance303.20035
MonotonicityNot monotonic
2024-10-31T18:23:26.036964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 138
 
4.4%
5 104
 
3.3%
15 97
 
3.1%
17 87
 
2.8%
12 86
 
2.8%
10 83
 
2.7%
16 83
 
2.7%
26 80
 
2.6%
29 78
 
2.5%
21 78
 
2.5%
Other values (89) 2196
70.6%
ValueCountFrequency (%)
1 3
 
0.1%
2 2
 
0.1%
3 51
 
1.6%
4 42
 
1.4%
5 104
3.3%
5.5 1
 
< 0.1%
6 138
4.4%
7 75
2.4%
8 63
2.0%
9 66
2.1%
ValueCountFrequency (%)
289 1
 
< 0.1%
124 1
 
< 0.1%
112 1
 
< 0.1%
108 3
0.1%
105 2
0.1%
101 2
0.1%
98 3
0.1%
97 1
 
< 0.1%
87 2
0.1%
81 3
0.1%

total sulfur dioxide
Real number (ℝ)

High correlation 

Distinct227
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.464148
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.6 KiB
2024-10-31T18:23:26.142531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile14
Q138
median90
Q3127
95-th percentile183
Maximum440
Range434
Interquartile range (IQR)89

Descriptive statistics

Standard deviation54.618018
Coefficient of variation (CV)0.61740287
Kurtosis-0.28277211
Mean88.464148
Median Absolute Deviation (MAD)45
Skewness0.39513864
Sum275123.5
Variance2983.1279
MonotonicityNot monotonic
2024-10-31T18:23:26.245523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 41
 
1.3%
111 39
 
1.3%
113 38
 
1.2%
15 34
 
1.1%
31 33
 
1.1%
24 33
 
1.1%
18 33
 
1.1%
20 33
 
1.1%
122 32
 
1.0%
23 30
 
1.0%
Other values (217) 2764
88.9%
ValueCountFrequency (%)
6 3
 
0.1%
7 4
 
0.1%
8 14
0.5%
9 15
0.5%
10 28
0.9%
11 26
0.8%
12 29
0.9%
13 28
0.9%
14 30
1.0%
15 34
1.1%
ValueCountFrequency (%)
440 1
< 0.1%
289 1
< 0.1%
278 1
< 0.1%
259 1
< 0.1%
251 1
< 0.1%
248 2
0.1%
243 1
< 0.1%
240 1
< 0.1%
230 1
< 0.1%
227 1
< 0.1%

density
Real number (ℝ)

High correlation 

Distinct833
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99485181
Minimum0.98711
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.6 KiB
2024-10-31T18:23:26.356085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.9896145
Q10.9925
median0.99546
Q30.9971375
95-th percentile0.9989
Maximum1
Range0.01289
Interquartile range (IQR)0.0046375

Descriptive statistics

Standard deviation0.0028979734
Coefficient of variation (CV)0.0029129699
Kurtosis-0.81420762
Mean0.99485181
Median Absolute Deviation (MAD)0.00206
Skewness-0.42680203
Sum3093.9891
Variance8.3982496 × 10-6
MonotonicityNot monotonic
2024-10-31T18:23:26.476978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9976 37
 
1.2%
0.9972 37
 
1.2%
0.9968 37
 
1.2%
0.9984 35
 
1.1%
0.998 32
 
1.0%
0.9964 29
 
0.9%
0.9962 28
 
0.9%
0.9978 27
 
0.9%
0.997 27
 
0.9%
0.9974 25
 
0.8%
Other values (823) 2796
89.9%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98722 1
< 0.1%
0.9874 1
< 0.1%
0.98742 2
0.1%
0.98746 2
0.1%
0.98758 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 2
0.1%
0.98816 1
< 0.1%
ValueCountFrequency (%)
1 10
0.3%
0.9999 1
 
< 0.1%
0.9998 10
0.3%
0.99976 1
 
< 0.1%
0.99975 1
 
< 0.1%
0.99974 1
 
< 0.1%
0.99971 2
 
0.1%
0.9997 8
0.3%
0.99966 1
 
< 0.1%
0.99965 1
 
< 0.1%

pH
Real number (ℝ)

Distinct98
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2380161
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.6 KiB
2024-10-31T18:23:26.601133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile2.97
Q13.13
median3.23
Q33.35
95-th percentile3.52
Maximum4.01
Range1.27
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.16521968
Coefficient of variation (CV)0.051024972
Kurtosis0.36850724
Mean3.2380161
Median Absolute Deviation (MAD)0.11
Skewness0.28198622
Sum10070.23
Variance0.027297542
MonotonicityNot monotonic
2024-10-31T18:23:26.716974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.26 94
 
3.0%
3.16 89
 
2.9%
3.22 88
 
2.8%
3.2 86
 
2.8%
3.36 83
 
2.7%
3.24 83
 
2.7%
3.18 79
 
2.5%
3.15 78
 
2.5%
3.23 77
 
2.5%
3.14 77
 
2.5%
Other values (88) 2276
73.2%
ValueCountFrequency (%)
2.74 1
 
< 0.1%
2.79 1
 
< 0.1%
2.8 1
 
< 0.1%
2.82 1
 
< 0.1%
2.83 4
 
0.1%
2.85 3
 
0.1%
2.86 7
0.2%
2.87 4
 
0.1%
2.88 11
0.4%
2.89 5
0.2%
ValueCountFrequency (%)
4.01 2
0.1%
3.9 2
0.1%
3.85 1
 
< 0.1%
3.78 2
0.1%
3.76 1
 
< 0.1%
3.75 3
0.1%
3.74 1
 
< 0.1%
3.72 3
0.1%
3.71 4
0.1%
3.7 1
 
< 0.1%

sulphates
Real number (ℝ)

High correlation 

Distinct108
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57107395
Minimum0.23
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.6 KiB
2024-10-31T18:23:26.820852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile0.36
Q10.46
median0.55
Q30.64
95-th percentile0.85
Maximum2
Range1.77
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.16587227
Coefficient of variation (CV)0.29045672
Kurtosis9.3880618
Mean0.57107395
Median Absolute Deviation (MAD)0.09
Skewness1.9345483
Sum1776.04
Variance0.02751361
MonotonicityNot monotonic
2024-10-31T18:23:26.931570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54 125
 
4.0%
0.5 120
 
3.9%
0.56 111
 
3.6%
0.52 101
 
3.2%
0.58 101
 
3.2%
0.6 100
 
3.2%
0.53 98
 
3.2%
0.48 92
 
3.0%
0.57 86
 
2.8%
0.55 84
 
2.7%
Other values (98) 2092
67.3%
ValueCountFrequency (%)
0.23 1
 
< 0.1%
0.25 1
 
< 0.1%
0.26 3
 
0.1%
0.27 6
 
0.2%
0.28 2
 
0.1%
0.29 5
 
0.2%
0.3 9
0.3%
0.31 15
0.5%
0.32 11
0.4%
0.33 17
0.5%
ValueCountFrequency (%)
2 1
 
< 0.1%
1.98 1
 
< 0.1%
1.95 2
0.1%
1.62 1
 
< 0.1%
1.61 1
 
< 0.1%
1.59 1
 
< 0.1%
1.56 1
 
< 0.1%
1.36 3
0.1%
1.34 1
 
< 0.1%
1.33 1
 
< 0.1%

alcohol
Real number (ℝ)

High correlation 

Distinct85
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.645598
Minimum8.4
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.6 KiB
2024-10-31T18:23:27.044971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.1
Q19.6
median10.5
Q311.4
95-th percentile12.8
Maximum14.9
Range6.5
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.2112905
Coefficient of variation (CV)0.11378323
Kurtosis-0.58775189
Mean10.645598
Median Absolute Deviation (MAD)0.9
Skewness0.53122813
Sum33107.81
Variance1.4672248
MonotonicityNot monotonic
2024-10-31T18:23:27.154855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 195
 
6.3%
9.4 168
 
5.4%
9.2 123
 
4.0%
11 117
 
3.8%
9.8 113
 
3.6%
10.5 107
 
3.4%
10 93
 
3.0%
11.2 88
 
2.8%
9.6 87
 
2.8%
10.4 86
 
2.8%
Other values (75) 1933
62.2%
ValueCountFrequency (%)
8.4 4
 
0.1%
8.5 2
 
0.1%
8.6 2
 
0.1%
8.7 17
 
0.5%
8.8 31
 
1.0%
8.9 16
 
0.5%
9 60
1.9%
9.05 1
 
< 0.1%
9.1 71
2.3%
9.2 123
4.0%
ValueCountFrequency (%)
14.9 1
 
< 0.1%
14.2 1
 
< 0.1%
14.05 1
 
< 0.1%
14 9
0.3%
13.9 2
 
0.1%
13.8 2
 
0.1%
13.7 3
 
0.1%
13.6 13
0.4%
13.55 1
 
< 0.1%
13.5 6
0.2%

quality
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7874598
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.6 KiB
2024-10-31T18:23:27.246867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.83168806
Coefficient of variation (CV)0.1437052
Kurtosis0.22040941
Mean5.7874598
Median Absolute Deviation (MAD)1
Skewness0.16564223
Sum17999
Variance0.69170503
MonotonicityNot monotonic
2024-10-31T18:23:27.334205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 1397
44.9%
5 1060
34.1%
7 482
 
15.5%
4 90
 
2.9%
8 68
 
2.2%
3 13
 
0.4%
ValueCountFrequency (%)
3 13
 
0.4%
4 90
 
2.9%
5 1060
34.1%
6 1397
44.9%
7 482
 
15.5%
8 68
 
2.2%
ValueCountFrequency (%)
8 68
 
2.2%
7 482
 
15.5%
6 1397
44.9%
5 1060
34.1%
4 90
 
2.9%
3 13
 
0.4%

Interactions

2024-10-31T18:23:23.059037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:11.750671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:13.017155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:14.007934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:15.099988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:15.995072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:17.130796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:18.064476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:18.983688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:20.168051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:21.118124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:22.105914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:23.136440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:11.835489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:13.100100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:14.086077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:15.171877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:16.076304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:17.210361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:18.138813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:19.069962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:20.252056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:21.202650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:22.186841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:23.218344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:12.202802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:13.184678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:14.296253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:15.252556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:16.157369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:17.289430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:18.218000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:19.158071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:20.334208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:21.298705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:22.269601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:23.533584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:12.283319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:13.262643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:14.376179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:15.330307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:16.235911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:17.367125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:18.291874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:19.238376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:20.410232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:21.379509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:22.346030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:23.604672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:12.363672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:13.338538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:14.456805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:15.398177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:16.323224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:17.444713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:18.361532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:19.313548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:20.483401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:21.460722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:22.424386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:23.684935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:12.467128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:13.420013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:14.536172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:15.471433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:16.401721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:17.528045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:18.434312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:19.399706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:20.557695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:21.541525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:22.510949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:23.764865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:12.552053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:13.501440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:14.619868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:15.548023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:16.480036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:17.606266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:18.508729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:19.478859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:20.638635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:21.623313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:22.590247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:23.840527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:12.624096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:13.578769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:14.700285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:15.618822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:16.552772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:17.677857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:18.579963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:19.558348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:20.713930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:21.699959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:22.667079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:23.917171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:12.705583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:13.673024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:14.786057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:15.697028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:16.789530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:17.755635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:18.666978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:19.835608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:20.794925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:21.781407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:22.748594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:24.006134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:12.781502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:13.757138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:14.864008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:15.773675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:16.877234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:17.831155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:18.749038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:19.919897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:20.880346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:21.860338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:22.828542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:24.092212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:12.861289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:13.841477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:14.946205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:15.850174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:16.969143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:17.909031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:18.829587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:20.009498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:20.959169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:21.945566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:22.908992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:24.174917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:12.940477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:13.928094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:15.023155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:15.922288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:17.047732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:17.987819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:18.908083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:20.090689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:21.041005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:22.024146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-31T18:23:22.985039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-31T18:23:27.406301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
alcoholchloridescitric aciddensityfixed acidityfree sulfur dioxidepHqualityresidual sugarsulphatestotal sulfur dioxidetypevolatile acidity
alcohol1.000-0.4070.098-0.667-0.206-0.0260.1070.481-0.156-0.015-0.1020.262-0.145
chlorides-0.4071.000-0.0760.6500.566-0.4470.272-0.310-0.1870.448-0.5180.7380.574
citric acid0.098-0.0761.0000.0480.2570.091-0.3450.1810.0670.1260.1350.483-0.422
density-0.6670.6500.0481.0000.587-0.2500.096-0.3060.2640.383-0.2650.6130.374
fixed acidity-0.2060.5660.2570.5871.000-0.421-0.117-0.116-0.1340.394-0.4670.5870.328
free sulfur dioxide-0.026-0.4470.091-0.250-0.4211.000-0.2790.1110.332-0.3210.7980.496-0.452
pH0.1070.272-0.3450.096-0.117-0.2791.000-0.075-0.2830.289-0.3990.4800.424
quality0.481-0.3100.181-0.306-0.1160.111-0.0751.0000.0680.0450.0130.192-0.341
residual sugar-0.156-0.1870.0670.264-0.1340.332-0.2830.0681.000-0.1950.4260.553-0.190
sulphates-0.0150.4480.1260.3830.394-0.3210.2890.045-0.1951.000-0.4220.5170.299
total sulfur dioxide-0.102-0.5180.135-0.265-0.4670.798-0.3990.0130.426-0.4221.0000.796-0.498
type0.2620.7380.4830.6130.5870.4960.4800.1920.5530.5170.7961.0000.684
volatile acidity-0.1450.574-0.4220.3740.328-0.4520.424-0.341-0.1900.299-0.4980.6841.000

Missing values

2024-10-31T18:23:24.280975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-31T18:23:24.442152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
0Moscatel8.10.240.3210.50.03034.0105.00.994073.110.4211.86
1Moscatel5.80.230.202.00.04339.0154.00.992263.210.3910.26
2Moscatel7.50.330.362.60.05126.0126.00.990973.320.5312.76
3Moscatel6.60.380.369.20.06142.0214.00.997603.310.569.45
4Moscatel6.40.150.291.80.04421.0115.00.991663.100.3810.25
5Moscatel6.50.320.345.70.04427.091.00.991843.280.6012.07
6Moscatel7.50.220.322.40.04529.0100.00.991353.080.6011.37
7Moscatel6.40.230.321.90.03840.0118.00.990743.320.5311.87
8Moscatel6.10.220.311.40.03940.0129.00.991933.450.5910.95
9Moscatel6.50.480.020.90.04332.099.00.992263.140.479.84
typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
3221Syrah6.60.7250.207.80.07329.079.00.997703.290.549.25
3222Syrah6.30.5500.151.80.07726.035.00.993143.320.8211.66
3223Syrah5.40.7400.091.70.08916.026.00.994023.670.5611.66
3224Syrah6.30.5100.132.30.07629.040.00.995743.420.7511.06
3225Syrah6.80.6200.081.90.06828.038.00.996513.420.829.56
3226Syrah6.20.6000.082.00.09032.044.00.994903.450.5810.55
3227Syrah5.90.5500.102.20.06239.051.00.995123.520.7611.26
3228Syrah6.30.5100.132.30.07629.040.00.995743.420.7511.06
3229Syrah5.90.6450.122.00.07532.044.00.995473.570.7110.25
3230Syrah6.00.3100.473.60.06718.042.00.995493.390.6611.06

Duplicate rows

Most frequently occurring

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
191Moscatel7.00.150.2814.700.05129.0149.00.997922.960.399.078
225Moscatel7.30.190.2713.900.05745.0155.00.998072.940.418.888
231Moscatel7.40.160.3013.700.05633.0168.00.998252.900.448.777
230Moscatel7.40.160.2715.500.05025.0135.00.998402.900.438.776
12Moscatel5.70.220.2016.000.04441.0113.00.998623.220.468.965
123Moscatel6.60.220.2317.300.04737.0118.00.999063.080.468.865
140Moscatel6.70.160.3212.500.03518.0156.00.996662.880.369.065
237Moscatel7.50.240.3113.100.05026.0180.00.998843.050.539.165
13Moscatel5.70.220.2216.650.04439.0110.00.998553.240.489.064
27Moscatel6.00.200.266.800.04922.093.00.992803.150.4211.064